Oxygen venular saturation correlates with a functional loss in primary open-angle glaucoma and normal-tension glaucoma patients

Chorioretinal Hypoxia Detection Using Lipid-Polymer Hybrid Organic Room-Temperature Phosphorescent Nanoparticles

Ischemia-induced hypoxia is a common complication associated with numerous diseases and is the most important prognostic factor in retinal vein occlusions (RVOs). Early detection and long-term visualization of retinal tissue hypoxia is essential to understand the pathophysiology and treatment of ischemic retinopathies. However, no effective solution exists to evaluate extravascular retinal tissue oxygen tension. Here, we demonstrate a lipid-polymer hybrid organic room-temperature phosphorescence (RTP) nanoparticle (NP) platform that optically detects tissue hypoxia in real-time with high signal-to-noise ratio. The fabricated NPs exhibit long-lived bright RTP, high sensitivity toward oxygen quenching, and desirable colloidal and optical stability. When tested as a hypoxia imaging probe in vivo using rabbit RVO and choroidal vascular occlusion (CVO) models via intravitreal and intravenous (IV) injections, respectively, its RTP signal is exclusively turned on where tissue hypoxia is present with a signal-to-noise ratio of 12.5. The RTP NP platform is compatible with multimodal imaging. No ocular or systemic complications are observed with either administration route. The developed organic RTP NPs present a novel platform approach that allows for biocompatible, nondestructive detection of tissue hypoxia and holds promise as a sensitive imaging tool to monitor longitudinal tissue oxygen levels and evaluate various hypoxia-driven vascular diseases.

Retinal oximetry in glaucoma: investigations and findings reviewed

Abnormalities of the retinal blood supply have been widely implicated in primary open-angle glaucoma (POAG). Impaired blood supply to the retina and optic nerve head (ONH) may be a primary pathophysiologic mechanism contributing to POAG ('vascular hypothesis'). However, the decreased metabolic activity of atrophic tissue is itself known to induce both vascular changes and decreased blood flow due to reduced oxygen demand. Therefore, primary nonvascular factors could potentially induce glaucomatous atrophy, with subsequent secondary vascular pathology ('mechanical hypothesis'). Retinal oximetry holds great promise in the investigation of glaucoma pathogenesis, as it can provide useful data on retinal metabolic oxygen demand, especially when combined with measurements of retinal blood flow. This review surveys the research on retinal metabolism in POAG using spectroscopic retinal oximetry. The use of mathematical models in combination with oximetric data to investigate the role of retinal metabolism and oxygen supply in POAG is also discussed.

Ocular blood flow as a clinical observation: Value, limitations and data analysis

Alterations in ocular blood flow have been identified as important risk factors for the onset and progression of numerous diseases of the eye. In particular, several population-based and longitudinal-based studies have provided compelling evidence of hemodynamic biomarkers as independent risk factors for ocular disease throughout several different geographic regions. Despite this evidence, the relative contribution of blood flow to ocular physiology and pathology in synergy with other risk factors and comorbidities (e.g., age, gender, race, diabetes and hypertension) remains uncertain. There is currently no gold standard for assessing all relevant vascular beds in the eye, and the heterogeneous vascular biomarkers derived from multiple ocular imaging technologies are non-interchangeable and difficult to interpret as a whole. As a result of these disease complexities and imaging limitations, standard statistical methods often yield inconsistent results across studies and are unable to quantify or explain a patient's overall risk for ocular disease. Combining mathematical modeling with artificial intelligence holds great promise for advancing data analysis in ophthalmology and enabling individualized risk assessment from diverse, multi-input clinical and demographic biomarkers. Mechanism-driven mathematical modeling makes virtual laboratories available to investigate pathogenic mechanisms, advance diagnostic ability and improve disease management. Artificial intelligence provides a novel method for utilizing a vast amount of data from a wide range of patient types to diagnose and monitor ocular disease. This article reviews the state of the art and major unanswered questions related to ocular vascular anatomy and physiology, ocular imaging techniques, clinical findings in glaucoma and other eye diseases, and mechanistic modeling predictions, while laying a path for integrating clinical observations with mathematical models and artificial intelligence. Viable alternatives for integrated data analysis are proposed that aim to overcome the limitations of standard statistical approaches and enable individually tailored precision medicine in ophthalmology.